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The transition from combustion engines to electric propulsion is accelerating in every coordinate of the globe. The engineers had strived hard to augment the engine performance for more than eight decades, and a similar challenge had emerged again for electric vehicles. To analyze the performance of the engine, the vector engine operating point (EOP) is defined, which is common industry practice, and the performance vector electric vehicle motor operating point (EVMOP) is not explored in the existing literature. In an analogous sense, electric vehicles are embedded with three primary components, e.g., Battery, Inverter, Motor, and in this article, the EVMOP is defined using the parameters [motor torque, motor speed, motor current]. As a second aspect of this research, deep learning models are developed to predict the EVMOP by mapping the parameters representing the dynamic state of the system in real-time.

Often, when assessing the distraction or ease of use of an in-vehicle task (such as entering a destination using the street address method), the first question is “How long does the task take on average?” Engineers routinely resolve this question using computational models. For in-vehicle tasks, “how long” is estimated by summing times for the included task elements (e.g., decide what to do, press a button) from SAE Recommended Practice J2365 or now using new static (while parked) data presented here. Times for the occlusion conditions in J2365 and the NHTSA Distraction Guidelines can be determined using static data and Pettitt’s Method or Purucker’s Method. These first approximations are reasonable and can be determined quickly. The next question usually is “How likely is it that the task will exceed some limit?”

The rise in demand for electric and hybrid vehicles, the issue of bearing currents in electric motors has become increasingly relevant. These vehicles use inverters with high frequency switch that generates the common mode voltage and current, the main factor responsible for bearing issues. In the machine structure, there are some parasitic capacitances that exist inherently. They provide a low impedance path for the generated current, which flows through the machine bearing. Investigating this problem in practical scenarios during the design stage is costly and requires great effort to measure these currents. For this reason, a strategy of analysis aided by electromagnetic simulation software can achieve desired results in terms of complexity and performance. This work proposes a methodology using Ansys Maxwell software to simulate two-dimensional (2D) and three-dimensional (3D) model of a three-phase permanent magnet motor with eight poles.

This paper presents a mechanical energy control volume analysis for incompressible flow around road vehicles using results from Detached Eddy Simulation Computational Fluid Dynamics calculations. The control volume approach equates the rate of work done by surface forces of the vehicle to (i) the rate of work and kinetic energy flux at the control volume boundaries (particularly in the vehicle wake) and (ii) the rate of energy loss in the domain. At the downstream control volume boundary, the wake terms can be divided into lift-induced and profile drag terms. The rate of energy loss in the domain can be used as a volumetric analog for drag (drag counts/m3, when normalized). This allows for a quantitative break down of the contributions of different flow features/regions to the overall drag force.

Prior work in the literature have shown that pre-chamber spark plug technologies can provide remarkable improvements in engine performance. In this work, three passively fueled pre-chamber spark plugs with different pre-chamber geometries were investigated using in-cylinder high-speed imaging of spectral emission in the visible wavelength region in a single-cylinder direct-injection spark-ignition gasoline engine. The effects of the pre-chamber spark plugs on flame development were analyzed by comparing the flame progress between the pre-chamber spark plugs and with the results from a conventional spark plug. The engine was operated at fixed conditions (relevant to federal test procedures) with a constant speed of 1500 revolutions per minute with a coolant temperature of 90 oC and stoichiometric fuel-to-air ratio. The in-cylinder images were captured with a color high-speed camera through an optical insert in the piston crown.

Electric vehicles (EVs) are a promising and proven technology for achieving sustainable mobility with zero carbon emissions, very low noise pollution, and reducing the dependency on fossil fuels. Global EV sales have been increasing by ~110 % since 2015, with a significant rise in 2021 (~6.75 mils EV registered) mainly led by China, the US, and Europe, amplifying the EV market share to 8.3% compared to 4.2% in 2020. Future developments aimed at designing better batteries and charging technologies that reduce charging time, reduce initial battery cost, and increased flexibility. In India, EVs are emerging significantly due to stringent Carbon di Oxide (CO2) reduction drives, increasing crude oil prices, and the availability of cheaper renewable energy. Leveraging government promotional policies, evolving the entire ecosystem, globally advantageous manufacturing costs, and competitive engineering skills form the perfect blend for India.

In order to decarbonize and lower the overall emissions of the transport sector, immediate and cost-effective powertrain solutions are needed. Natural gas offers the advantage of a direct reduction of carbon dioxide (CO2) emissions due to its better Carbon to Hydrogen ratio (C/H) compared to common fossil fuels, e.g. gasoline or diesel. Moreover, an optimized engine design suiting the advantages of natural gas in knock resistance and lean mixtures keeping in mind the challenges of power density, efficiency and cold start manoeuvres. In the public funded project MethMag (Methane lean combustion engine) a gasoline fired three-cylinder-engine is redesigned based on this change of requirements and benchmarked against the previous gasoline engine.

Advanced features in automotive systems often necessitate the management of complex interactions between subsystems. Existing control strategies are designed for certain levels of robustness, however their performance can unexpectedly deteriorate in the presence of significant uncertainties, resulting in undesirable system behaviors. This limitation is further amplified in systems with complex nonlinear dynamics. Hydro-mechanical clutch actuators are among those systems whose behaviors are highly sensitive to variations in subsystem characteristics and operating environments. In a P2 hybrid propulsion system, a wet clutch is utilized for cranking the engine during an EV-HEV mode switching event. It is critical that the hydro-mechanical clutch actuator is stroked as quickly and as consistently as possible despite the existence of uncertainties. Thus, the quantification of uncertainties on clutch actuator behaviors is important for enabling smooth EV-HEV transitions.

Mobility in the automotive and transportation sectors has been experiencing a period of unprecedented evolution. A growing need for efficient, clean and safe mobility has increased momentum toward sustainable technologies in these sectors. Toward this end, battery electric vehicles have drawn keen interest and their market share is expected to grow significantly in the coming years, especially in light-duty applications such as passenger cars. Although the battery electric vehicles feature high performance and zero tailpipe emission characteristics, economic and technical issues such as battery cost, driving range, recharging time and infrastructure remain main hurdles that need to be fully addressed. In particular, the low power density of the battery limits its broad adoption in heavy-duty applications such as class 8 semi-trailer trucks due to the required size and weight of the battery and electric motor.

Development of propulsion control systems frequently involves large-scale transient simulations, e.g. Monte Carlo simulations or drive-cycle optimizations, which require fast dynamic plant models. Models of the air-path—for internal combustion engines or fuel cells—can exhibit stiff behavior, though, causing slow numerical simulations due to either using an implicit solver or sampling much faster than the bandwidth of interest to maintain stability. This paper proposes a method to reduce air-path model stiffness by adding an impedance in series with potentially stiff components, e.g. throttles, valves, compressors, and turbines, thereby allowing the use of a fast-explicit solver. An impedance, by electrical analogy, is a frequency-dependent resistance to flow, which is shaped to suppress the high-frequency dynamics causing air-path stiffness, while maintaining model accuracy in the bandwidth of interest.

By utilizing the vehicle to infrastructure communication, the conventional Green Light Optimized Speed Advisory (GLOSA) applications give speed advisory range for drivers to travel to pass at the green light. However, these systems do not consider the traffic between the ego vehicle and the traffic light location, resulting in inaccurate speed advisories. Therefore, the driver needs to intuitively adjust the vehicle's speed to pass at the green light and avoid traffic in these scenarios. Furthermore, inaccurate speed advisories may result in unnecessary acceleration and deceleration, resulting in poor fuel efficiency and comfort. To address these shortcomings of conventional GLOSA, in this study, we proposed the utilization of collaborative perception messages shared by smart infrastructures to create an enhanced speed advisory for the connected vehicle drivers and automated vehicles.

This paper presents a method of using CAE to determine the pre-load and torque applied to a U-Bolt rear Spring Seat. In this paper it is review two U-bolt design and the stresses generated by the pre-load torque applied, based in this study a process to determine the minimal preload and the torque is discussed. By this process it is possible to determine the minimum Torque and the correct pre-load in the U-Bolt element and assuring the correct fastening of the components avoiding over stress in the Bar elements.

Trapped air bubbles inside coolant systems have adverse effect on the cooling performance. Hence, it is imperative to ensure an effective filling and de-aeration of the coolant system in order to have less air left before the operation of the coolant system. In the present work, a coolant/air multiphase VOF method was utilized using the commercial CFD software SimericsMP+® to study the coolant filling and subsequent de-aeration process in a Battery Electric Vehicle (BEV) cooling system. First, validations of the numerical simulations against experiments were performed for a simplified coolant recirculation system. This system uses a tequila bottle for de-aeration and the validations were performed for different coolant flow rates to examine the de-aeration efficiency. A similar trend of de-aeration was captured between simulation and experimental measurement.

Most of the applications of magnesium in lightweighting commercial cars and trucks are die castings rather than sheet metal, and automotive applications of magnesium sheet have typically been experimental or low-volume serial production. The overarching objective of this collaborative research project organized by the United States Automotive Materials Partnership (USAMP) was to develop new low-cost magnesium alloys, and demonstrate warm-stamping of magnesium sheet inner and outer door panels for a 2013 MY Ford Fusion at a fully accounted integrated component cost increase over conventional steel stamped components of no more than $2.50/lb. saved ($5.50/kg saved). The project demonstrated the computational design of new magnesium (Mg) alloys from atomistic levels, cast new experimental alloy ingots and explored thermomechanical rolling processes to produce thin Mg sheet of desired textures.

The automotive industry is passing for a big transformation, due to technologies advance. The electrical technologies are also on a good rising curve, calling the attention of the Original Equipment Manufacturer (OEMs). This scenario generates the demand for a faster method to train their new hired engineers, when compared with usual on the job training. Model in the Loop (MiL) consists in one of the real-time embedded systems test phases, which is developed in a computational environment, performing a mathematical modeling of the system, presenting an interface that allows the visualization of its dynamics and the signals involved. Two powerful software in industry that apply MiL are the Matlab and Simulink. A project involving these applications was proposed for a team of new hired engineers, developing models of several vehicle Electronic Control Units (ECUs), with some scope reduction as an example the functional requirements reduction.

Fuel power consumption for light duty vehicles has previously been shown to be proportional to vehicle traction power, with an offset for overhead and accessory losses. This allows the fuel consumption for an individual powertrain to be projected across different vehicles, missions, and drive cycles. This work applies the power-based model to commercial vehicles and demonstrates its usefulness for projecting fuel consumption on both regulatory and customer use cycles. The ability to project fuel consumption to different missions is particularly useful for commercial vehicles, as they are used in a wide range of applications and with customized designs. Specific cases are investigated for Light and Medium Heavy- Duty work trucks. The average power required by a vehicle to drive the regulatory cycles varies by nearly a factor 10 between the Class 4 vehicle on the ARB Transient cycle and the loaded Class 7 vehicle at 65 mph on grade.

Model-guided development of drivetrain control and calibration is a key enabler of robust and efficient vehicle design process. A number of CAE tools are available today for modeling hydro-mechanical systems. Automatic transmission behaviors are well understood to effectively tune the model parameters for targeted applications. Drivetrain models provide physical insight for understanding the effects of component interactions on system behaviors. They are also widely used in HIL/SIL environments to debug control strategies. Nonetheless, it is still a challenge to predict shift quality, especially during a sequence of multiple events, with enough accuracy to support model-guided control design and calibration. The inclusion of hydraulic circuits in simulation models often results in challenges for numerical simulation.

Seat strength has increased over the past four decades which includes a transition to dual recliners. There are seat collision performance issues with stiff ABTS and very strong seats in rear impacts with different occupant sizes, seating positions and physical conditions. In this study, eight rear sled tests were conducted in four series: 1) ABTS in a 56 km/h (35 mph) test with a 50th Hybrid III ATD at MGA, 2) dual-recliner ABTS and F-150 in a 56 km/h (35 mph) test with a 5th female Hybrid III ATD at Ford, 3) dual-recliner ABTS in a 48 km/h (30 mph) test with a 95th Hybrid III ATD leaning inboard at CAPE and 4) dual-recliner ABTS and Escape in 40 km/h (25 mph) in-position and out-of-position tests with a 50th Hybrid III ATD at Ford. The sled tests showed that single-recliner ABTS seats twist in severe rear impacts with the pivot side deformed more rearward than the stanchion side.

Innovations in mobility are built upon a management of complex interactions between sub-systems and components. A need for CAE tools that are capable of system simulations is well recognized, as evidenced by a growing number of commercial packages. However impressive they are, the predictability of such simulations still rests on the representation of the base components. Among them, a wet clutch actuator continues to play a critical role in the next generation propulsion systems. It converts hydraulic pressure to mechanical force to control torque transmitted through a clutch pack. The actuator is typically modeled as a hydraulic piston opposed by a mechanical spring. Because the piston slides over a seal, some models have a framework to account for seal friction. However, there are few contributions to the literature that describe the effects of seals on clutch actuator behaviors.

A wet clutch model is required in automotive propulsion system simulations for enabling robust design and control development. It commonly assumes Coulomb friction for simplicity, even though it does not represent the physics of hydrodynamic torque transfer. In practice, the Coulomb friction coefficient is treated as a tuning parameter in simulations to match vehicle data for targeted conditions. The simulations tend to deviate from actual behaviors for different drive conditions unless the friction coefficient is adjusted repeatedly. Alternatively, a complex hydrodynamic model, coupled with a surface contact model, is utilized to enhance the fidelity of system simulations for broader conditions. The theory of elastic asperity deformation is conventionally employed to model clutch surface contact. However, recent examination of friction material shows that the elastic modulus of surface fibers significantly exceeds the contact load, implying no deformation of fibers.